• Class 1: Overview of Python and its role in AI/ML
Learn why Python is the language of choice for AI and ML, and explore the fundamental
libraries used in data science.
• Class 2: Python syntax, variables, and data types
Review Python’s basic syntax, data types (lists, tuples, dictionaries), and control structures
(loops, conditions).
• Class 3: Introduction to NumPy and Pandas for data analysis
Learn how to use NumPy for numerical computations and Pandas for data manipulation and
analysis.
• Class 4: Working with data frames in Pandas
Learn how to load, manipulate, and explore datasets using Pandas DataFrames.
• Class 5: Visualizing data with Matplotlib
Learn how to visualize data using Matplotlib and create basic plots like histograms, scatter
plots, and line charts.

• Class 6: Handling missing data
Learn techniques to handle missing or null data in datasets, including
imputation methods.
• Class 7: Data scaling and normalization
Learn how to scale and normalize data to prepare it for machine learning models.
• Class 8: Encoding categorical variables
Explore methods for encoding categorical variables, such as one-hot encoding and label
encoding.
• Class 9: Splitting data into training and test sets
Learn how to split datasets into training and testing sets for model evaluation.
• Class 10: Feature selection techniques
Understand how to select important features to improve model performance and reduce
overfitting.

• Class 11: Introduction to supervised learning
Learn the basics of supervised learning and its types: regression and classification.
• Class 12: Linear regression model
Implement a linear regression model to predict continuous values based on input features.
• Class 13: Logistic regression model
Learn how to implement a logistic regression model for binary classification tasks.
• Class 14: Decision trees for classification
Understand decision tree algorithms and how they are used for classification tasks.
• Class 15: K-Nearest Neighbors (KNN) algorithm
Explore the KNN algorithm, a simple yet effective method for classification and regression.

• Class 16: Clustering and K-means algorithm
Learn how to group data into clusters using the K-means clustering algorithm.
• Class 17: Dimensionality reduction with PCA
Understand how Principal Component Analysis (PCA) reduces the dimensionality of data
while retaining important information.
• Class 18: Anomaly detection and outliers
Learn techniques for detecting anomalies and outliers in datasets.
• Class 19: Association rules in unsupervised learning
Discover how to find relationships between variables using association rule mining.
• Class 20: Introduction to Neural Networks
Get an overview of neural networks and how they mimic the human brain to solve
complex problems.

•Class 21: Introduction to deep learning
Understand the concepts of deep learning, artificial neural networks (ANN), and their applications.
•Class 22: Understanding artificial neural networks
Learn how artificial neural networks function and their building blocks: neurons, weights, and
activation functions.
•Class 23: Building and training a simple neural network
Build a simple neural network from scratch and train it on a dataset.
•Class 24: Convolutional Neural Networks (CNNs)
Dive into CNNs, a deep learning architecture specialized for image recognition tasks.
•Class 25: Recurrent Neural Networks (RNNs)
Learn about RNNs and their applications in sequence data, such as time series and text.